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E-grāmata: Evidence Synthesis for Decision Making in Healthcare

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(School of Social and ), (University of Leicester, UK), (Department of Health Sciences, University of Leicester), (Department of Health Sciences, University of Leicester), (School of Social and Community Medicine, University of Bristol)
  • Formāts: EPUB+DRM
  • Sērija : Statistics in Practice
  • Izdošanas datums: 12-Apr-2012
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118305409
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Evidence Synthesis for Decision Making in HealthcarePalielināt
  • Formāts: EPUB+DRM
  • Sērija : Statistics in Practice
  • Izdošanas datums: 12-Apr-2012
  • Izdevniecība: John Wiley & Sons Inc
  • Valoda: eng
  • ISBN-13: 9781118305409
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In the evaluation of healthcare, rigorous methods of quantitative assessment are necessary to establish interventions that are both effective and cost-effective. Usually a single study will not fully address these issues and it is desirable to synthesize evidence from multiple sources. This book aims to provide a practical guide to evidence synthesis for the purpose of decision making, starting with a simple single parameter model, where all studies estimate the same quantity (pairwise meta-analysis) and progressing to more complex multi-parameter structures (including meta-regression, mixed treatment comparisons, Markov models of disease progression, and epidemiology models). A comprehensive, coherent framework is adopted and estimated using Bayesian methods. Key features:





A coherent approach to evidence synthesis from multiple sources. Focus is given to Bayesian methods for evidence synthesis that can be integrated within cost-effectiveness analyses in a probabilistic framework using Markov Chain Monte Carlo simulation. Provides methods to statistically combine evidence from a range of evidence structures. Emphasizes the importance of model critique and checking for evidence consistency. Presents numerous worked examples, exercises and solutions drawn from a variety of medical disciplines throughout the book. WinBUGS code is provided for all examples.

Evidence Synthesis for Decision Making in Healthcare is intended for health economists, decision modelers, statisticians and others involved in evidence synthesis, health technology assessment, and economic evaluation of health technologies.
Preface xi
1 Introduction 1(16)
1.1 The rise of health economics
1(3)
1.2 Decision making under uncertainty
4(5)
1.2.1 Deterministic models
4(2)
1.2.2 Probabilistic decision modelling
6(3)
1.3 Evidence-based medicine
9(1)
1.4 Bayesian statistics
10(1)
1.5 NICE
11(1)
1.6 Structure of the book
12(1)
1.7 Summary key points
13(1)
1.8 Further reading
13(1)
References
14(3)
2 Bayesian methods and WinBUGS 17(26)
2.1 Introduction to Bayesian methods
17(9)
2.1.1 What is a Bayesian approach?
17(1)
2.1.2 Likelihood
18(1)
2.1.3 Bayes' theorem and Bayesian updating
19(3)
2.1.4 Prior distributions
22(1)
2.1.5 Summarising the posterior distribution
23(1)
2.1.6 Prediction
24(1)
2.1.7 More realistic and complex models
24(1)
2.1.8 MCMC and Gibbs sampling
25(1)
2.2 Introduction to WinBUGS
26(13)
2.2.1 The BUGS language
26(5)
2.2.2 Graphical representation
31(1)
2.2.3 Running WinBUGS
32(1)
2.2.4 Assessing convergence in WinBUGS
33(3)
2.2.5 Statistical inference in WinBUGS
36(3)
2.2.6 Practical aspects of using WinBUGS
39(1)
2.3 Advantages and disadvantages of a Bayesian approach
39(1)
2.4 Summary key points
40(1)
2.5 Further reading
41(1)
2.6 Exercises
41(1)
References
42(1)
3 Introduction to decision models 43(33)
3.1 Introduction
43(1)
3.2 Decision tree models
44(1)
3.3 Model parameters
45(7)
3.3.1 Effects of interventions
45(5)
3.3.2 Quantities relating to the clinical epidemiology of the clinical condition being treated
50(2)
3.3.3 Utilities
52(1)
3.3.4 Resource use and costs
52(1)
3.4 Deterministic decision tree
52(4)
3.5 Stochastic decision tree
56(10)
3.5.1 Presenting the results of stochastic economic decision models
60(6)
3.6 Sources of evidence
66(4)
3.7 Principles of synthesis for decision models (motivation for the rest of the book)
70(1)
3.8 Summary key points
70(1)
3.9 Further reading
71(1)
3.10 Exercises
71(1)
References
72(4)
4 Meta-analysis using Bayesian methods 76(18)
4.1 Introduction
76(2)
4.2 Fixed Effect model
78(3)
4.3 Random Effects model
81(6)
4.3.1 The predictive distribution
83(1)
4.3.2 Prior specification for τ
84(1)
4.3.3 'Exact' Random Effects model for Odds Ratios based on a Binomial likelihood
84(2)
4.3.4 Shrunken study level estimates
86(1)
4.4 Publication bias
87(1)
4.5 Study validity
88(1)
4.6 Summary key points
88(1)
4.7 Further reading
88(1)
4.8 Exercises
89(3)
References
92(2)
5 Exploring between study heterogeneity 94(21)
5.1 Introduction
94(1)
5.2 Random effects meta-regression models
95(9)
5.2.1 Generic random effect meta-regression model
95(3)
5.2.2 Random effects meta-regression model for Odds Ratio (OR) outcomes using a Binomial likelihood
98(2)
5.2.3 Autocorrelation and centring covariates
100(4)
5.3 Limitations of meta-regression
104(1)
5.4 Baseline risk
105(5)
5.4.1 Model for including baseline risk in a meta-regression on the (log) OR scale
107(2)
5.4.2 Final comments on including baseline risk as a covariate
109(1)
5.5 Summary key points
110(1)
5.6 Further reading
110(1)
5.7 Exercises
110(3)
References
113(2)
6 Model critique and evidence consistency in random effects meta-analysis 115(23)
6.1 Introduction
115(2)
6.2 The Random Effects model revisited
117(4)
6.3 Assessing model fit
121(3)
6.3.1 Deviance
121(1)
6.3.2 Residual deviance
122(2)
6.4 Model comparison
124(3)
6.4.1 Effective number of parameters, pD
125(1)
6.4.2 Deviance Information Criteria
126(1)
6.5 Exploring inconsistency
127(7)
6.5.1 Cross-validation
128(3)
6.5.2 Mixed predictive checks
131(3)
6.6 Summary key points
134(1)
6.7 Further reading
134(1)
6.8 Exercises
134(3)
References
137(1)
7 Evidence synthesis in a decision modelling framework 138(13)
7.1 Introduction
138(1)
7.2 Evaluation of decision models: One-stage vs two-stage approach
139(8)
7.3 Sensitivity analyses (of model inputs and model specifications)
147(1)
7.4 Summary key points
147(1)
7.5 Further reading
147(1)
7.6 Exercises
147(2)
References
149(2)
8 Multi-parameter evidence synthesis 151(18)
8.1 Introduction
151(1)
8.2 Prior and posterior simulation in a probabilistic model: Maple Syrup Urine Disease (MSUD)
152(3)
8.3 A model for prenatal HIV testing
155(6)
8.4 Model criticism in multi-parameter models
161(2)
8.5 Evidence-based policy
163(1)
8.6 Summary key points
164(1)
8.7 Further reading
165(1)
8.8 Exercises
166(1)
References
167(2)
9 Mixed and indirect treatment comparisons 169(24)
9.1 Why go beyond 'direct' head-to-head trials?
169(3)
9.2 A fixed treatment effects model for MTC
172(6)
9.2.1 Absolute treatment effects
176(1)
9.2.2 Relative treatment efficacy and ranking
176(2)
9.3 Random Effects MTC models
178(1)
9.4 Model choice and consistency of MTC evidence
179(2)
9.4.1 Techniques for presenting and understanding the results of MTC
180(1)
9.5 Multi-arm trials
181(1)
9.6 Assumptions made in mixed treatment comparisons
182(1)
9.7 Embedding an MTC within a cost-effectiveness analysis
183(2)
9.8 Extension to continuous, rate and other outcomes
185(2)
9.9 Summary key points
187(1)
9.10 Further reading
187(2)
9.11 Exercises
189(1)
References
190(3)
10 Markov models 193(34)
10.1 Introduction
193(2)
10.2 Continuous and discrete time Markov models
195(1)
10.3 Decision analysis with Markov models
196(3)
10.3.1 Evaluating Markov models
197(2)
10.4 Estimating transition parameters from a single study
199(7)
10.4.1 Likelihood
202(1)
10.4.2 Priors and posteriors for multinomial probabilities
202(4)
10.5 Propagating uncertainty in Markov parameters into a decision model
206(3)
10.6 Estimating transition parameters from a synthesis of several studies
209(15)
10.6.1 Challenges for meta-analysis of evidence on Markov transition parameters
209(2)
10.6.2 The relationship between probabilities and rates
211(2)
10.6.3 Modelling study effects
213(2)
10.6.4 Synthesis of studies reporting aggregate data
215(2)
10.6.5 Incorporating studies that provide event history data
217(2)
10.6.6 Reporting results from a Random Effects model
219(1)
10.6.7 Incorporating treatment effects
220(4)
10.7 Summary key points
224(1)
10.8 Further reading
224(1)
10.9 Exercises
224(1)
References
225(2)
11 Generalised evidence synthesis 227(24)
11.1 Introduction
227(3)
11.2 Deriving a prior distribution from observational evidence
230(3)
11.3 Bias allowance model for the observational data
233(5)
11.4 Hierarchical models for evidence from different study designs
238(6)
11.5 Discussion
244(1)
11.6 Summary key points
244(1)
11.7 Further reading
245(1)
11.8 Exercises
246(2)
References
248(3)
12 Expected value of information for research prioritisation and study design 251(19)
12.1 Introduction
251(5)
12.2 Expected value of perfect information
256(3)
12.3 Expected value of partial perfect information
259(5)
12.3.1 Computation
261(3)
12.3.2 Notes on EVPPI
264(1)
12.4 Expected value of sample information
264(2)
12.4.1 Computation
265(1)
12.5 Expected net benefit of sampling
266(1)
12.6 Summary key points
267(1)
12.7 Further reading
268(1)
12.8 Exercises
268(1)
References
268(2)
Appendix 1 Abbreviations 270(2)
Appendix 2 Common distributions 272(6)
A2.1 The Normal distribution
272(1)
A2.2 The Binomial distribution
273(1)
A2.3 The Multinomial distribution
273(1)
A2.4 The Uniform distribution
274(1)
A2.5 The Exponential distribution
274(1)
A2.6 The Gamma distribution
275(1)
A2.7 The Beta distribution
276(1)
A2.8 The Dirichlet distribution
277(1)
Index 278
Nicky Welton, Department of Social Medicine, University of Bristol Dr Welton's research includes Bayesian statistical modeling in epidemiology and evidence synthesis and evidence consistency.

Alex Sutton, Department of Health Sciences, University of Leicester Dr Sutton, senior lecture in medical statistics, has a primary research interest in meta-analysis. This specifically includes methods to combine evidence from disparate sources, and methods to deal with the problem of publication bias. With numerous published papers in a variety of journals he has also collaborated on over 15 substantive evidence synthesis projects. He is lead author on one of the first textbooks on meta-analysis in medicine and is co-editor on a recently published Wiley book on publication bias.

Nicola Cooper, Department of Health Sciences, University of Leicester Dr Coopers primary research interest is in the interface and integration of medical statistics and health economics. This specifically includes methods for statistical modelling of cost data, integration of evidence synthesis within a decision-modelling context, handling of missing data in economic evaluations conducted alongside clinical trials, and the application of Bayesian statistical methods to all of the above.

Keith Abrams, Department of Health Sciences, University of Leicester Professor Abrams' research interests include the development and application of Bayesian methods in healthcare evaluation, systematic reviews and meta-analysis, and the joint modeling of longitudinal and time-to-event data. He has published dozens of articles in numerous international journals and is the co-author of two Wiley books in this area.

Anthony E Ades, Department of Social Medicine, University of Bristol with over 30 published articles in the last three years, Professor Ades' research interests include statistical methods for multi-parameter evidence synthesis in epidemiology, disease mapping and economic evaluation; Bayesian decision theory and the expected value of information; statistical and epidemiological methods in infectious disease surveillance.
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